{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPUs, 2 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "200/200 [==============================] - 4s 18ms/step - loss: 1.9084 - acc: 0.8422 - val_loss: 4.1255 - val_acc: 0.8040 - time: 3.5364\n",
      "Testing...\n",
      "1/1 [==============================] - 0s 74ms/step - test_loss: 3.7414 - test_acc: 0.8380 - time: 0.0736\n",
      "Test loss 3.7414, Test accuracy 83.80%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import SBVAT\n",
    "model = SBVAT(graph, n_samples=50, attr_transform=\"normalize_attr\", device='GPU:0', seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=200)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"gcn\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attr_matrix (InputLayer)        [(None, 1433)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "adj_matrix (InputLayer)         [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "graph_convolution (GraphConvolu (None, 16)           22928       attr_matrix[0][0]                \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 16)           0           graph_convolution[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "graph_convolution_1 (GraphConvo (None, 7)            112         dropout[0][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "node_index (InputLayer)         [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "gather (Gather)                 (None, 7)            0           graph_convolution_1[0][0]        \n",
      "                                                                 node_index[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 23,040\n",
      "Trainable params: 23,040\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
